Create, test and run advanced experiments
The Carnetsoft research driving simulator is a programmable, fully interactive driving simulator with 210 degrees surround graphics HD rendering over 6 channels: left, center and right view plus 3 rearview mirrors on each monitor. That’s a resolution of 5760×1080 pixels. Click here to ask a question, order a simulator or just the software.
Create and run car driving behaviour and human factors experiments, and increase your productivity as a researcher. Experiments can be created quickly and results are analyzed efficiently. The toolset enables you to define experiments in a wide range of subjects.
Experiments can be created in the domains of
- Experimental Psychology (studies on attention, vigilance, workload, perception),
- Social Psychology (measures to influence driving behaviour),
- Clinical Psychology and Psychiatry (effects on exposure on phobias and anxieties while driving),
- Human Factors research (interfaces, secondary tasks, workload, autonomous driving),
- Pharmacology (effects of alcohol and drugs on behaviour), studies on training and learning, studies on driver fitness,
- Studies into autonomous driving and driver assistance systems,
And all of this for a price every human factors and experimental psychology research group can afford. The software costs 2500 euro, which is a fraction of the cost of comparable research driving simulator software licences. In addition to testing your research questions and helping you to make valuable contributions to science and create publications, this research instrument is very suitable for education and training of research skills. Carnetsoft takes active measures to reduce the incidence of simulator sickness.
The strong points of this research driving simulator software
- The most affordable research simulator in the market: the complete research simulator software modules plus runtime simulation software costs only €2500, which is a fraction of the price of comparable alternatives
- flexible and quick design and testing of experiment with an easy-to-learn script language
- extensive tools, including a database designer to create roads and virtual environments
- a large number of variables can be sampled including time headway, time to collision, brake reaction time, time to line crossing, etc. In addition you can create your own dependent variables to sample with 10 Hz, or do all data analysis in real-time during the experiment, and save the output in an external file
- 3-display surround graphics of high quality with 5760×1080 resolution, running at 60 Hz
- realistic artificially intelligent traffic
- a large number of standard databases and experiment scripts that will get you running quickly
- python scripts of the rendering engine are included so you have full access to the graphics rendering
- flexible support via email and skype/teamviewer that greatly helps you with the development of experiments.
- various levels of autonomous driving and driving support can be selected and programmed.
Examples of recent publications
The research simulator of Carnetsoft has been used by researchers to produce a large number of publications in very different fields of driver centered behavioural research. These studies include experiments that have used the research simulator software together with eye trackers, MATLAB applications and EEG recordings. Various different fields of science have been studied, such as research aimed at theory development, driver assistance systems and autonomous driving, older drivers, dyslexia, virtual reality, multitasking and driver distraction, driver fatigue and vigilance, etc.
Below is a list of recent examples of scientific publications with research that has applied the Carnetsoft research simulator:
- van Winsum, W. (2019). A Threshold Model For Stimulus Detection In The Peripheral Detection Task. Transportation Research Part F: Traffic Psychology and Behaviour, 65, 485-502, https://doi.org/10.1016/j.trf.2019.08.014
- van Winsum, W. (2019). The effects of optic flow on peripheral detection performance. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 626-636.
- van Winsum, W. (2019). Optic flow and tunnel vision in the Detection Response Task. Human Factors, 61(6), 992–1003. https://doi.org/10.1177/0018720818825008. See here for the full article of the peer reviewed and accepted version.
- Tejero, P., Insa, B., & Roca, J. (2019). Difficulties of Drivers With Dyslexia When Reading Traffic Signs: Analysis of Reading, Eye Gazes, and Driving Performance. Journal of Learning Disabilities,52(1), 84-95.
- J. Harvy, N. Thakor, A. Bezerianos and J. Li, “Between-Frequency Topographical and Dynamic High-Order Functional Connectivity for Driving Drowsiness Assessment,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 3, pp. 358-367, March 2019. doi: 10.1109/TNSRE.2019.2893949.
- van Winsum, W. (2018). The effects of cognitive and visual workload on peripheral detection in the Detection Response Task. Human Factors, 60 (6), 855-869. See here for the full article of the peer reviewed and accepted version.
- A. Lemkaddem et al., “Multi-modal driver drowsiness detection: A feasibility study,” 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV, 2018, pp. 9-12. doi: 10.1109/BHI.2018.8333357
- Seunghyeok Hong, Hyunbin Kwon, Sang Ho Choi, Kwang Suk Park,
Intelligent system for drowsiness recognition based on ear canal electroencephalography with photoplethysmography and electrocardiography, Information Sciences, Volume 453, 2018,
pp. 302-322, ISSN 0020-0255, https://doi.org/10.1016/j.ins.2018.04.003.
- Tran, Duy & Du, Jianhao & Sheng, Weihua & Osipychev, Denis & Sun, Yuge & Bai, he. (2018). A Human-Vehicle Collaborative Driving Framework for Driver Assistance. IEEE Transactions on Intelligent Transportation Systems. PP. 1-16. 10.1109/TITS.2018.2878027.
- Wechsler, K., Drescher, U., Janouch, C., Haeger, M., Voelcker-Rehage, C., Bock, O.L. (2018). Multitasking During Simulated Car Driving: A Comparison of Young and Older Persons. Front. Psychol., 15 June.
- J. Harvy, E. Sigalas, N. Thakor, A. Bezerianos and J. Li, “Performance Improvement of Driving Fatigue Identification Based on Power Spectra and Connectivity Using Feature Level and Decision Level Fusions,” 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Honolulu, HI, 2018, pp. 102-105. doi: 10.1109/EMBC.2018.8512259.
- D. Tran, H. Manh Do, W. Sheng, H. Bai and G. Chowdhary, “Real-time detection of distracted driving based on deep learning,” in IET Intelligent Transport Systems, vol. 12, no. 10, pp. 1210-1219, 12 2018. doi: 10.1049/iet-its.2018.5172
- Janouch, Christin & Drescher, Uwe & Wechsler, Konstantin & Haeger, Mathias & Bock, Otmar & Voelcker-Rehage, Claudia. (2018). Cognitive—Motor Interference in an Ecologically Valid Street Crossing Scenario. Frontiers in Psychology. 9. 10.3389/fpsyg.2018.00602.
- Haeger, Mathias & Bock, Otmar & Memmert, Daniel & Hüttermann, Stefanie. (2018). Can Driving-Simulator Training Enhance Visual Attention, Cognition, and Physical Functioning in Older Adults?. Journal of Aging Research. 2018. 1-9. 10.1155/2018/7547631.
- Tejero, Pilar & Insa, Beatriz & Roca, Javier. (2018). Difficulties of Drivers With Dyslexia When Reading Traffic Signs: Analysis of Reading, Eye Gazes, and Driving Performance. Journal of Learning Disabilities. 002221941876576. 10.1177/0022219418765766.
- Bock, O.; Drescher U.; van Winsum, W.; Kesnerus, K.; Voelcker-Rehage, C. (2018). A Virtual-Reality Approach for the Assessment and Rehabilitation of Multitasking Deficits. International Journal of Virtual and Augmented Reality, 2(1).
- Bock, O.; Drescher, U.; Janouch, C.; Haeger, M.; van Winsum, W.; Voelcker-Rehage C. (2018). An experimental paradigm for the assessment of realistic human multitasking. Virtual Reality.
- Elkosantini, S., & Darmoul, S. (2018). A new framework for the computer modelling and simulation of car driver behavior. SIMULATION,94(12), 1081–1097.https://doi.org/10.1177/0037549717748747
- Tejero, Pilar & Insa, Beatriz & Roca, Javier. (2018). Increasing the default interletter spacing of words can help drivers to read traffic signs at longer distances. Accident Analysis and prevention. 117. 298-303. 10.1016/j.aap.2018.04.028.
- Roca, J & Tejero, P & Insa, B. (2018). Accident ahead? Difficulties of drivers with and without reading impairment recognising words and pictograms in variable message signs. Applied Ergonomics. 67. 83-90. 10.1016/j.apergo.2017.09.013.
- Wang, Hongtao & Dragomir, Andrei & Itrat Abbasi, Nida & Li, Junhua & Thakor, N.v & Bezerianos, Anastasios. (2018). A novel real-time driving fatigue detection system based on wireless dry EEG. Cognitive Neurodynamics. 12. 10.1007/s11571-018-9481-5.
- Roca, Javier & Insa, Beatriz & Tejero, Pilar. (2018). Legibility of Text and Pictograms in Variable Message Signs: Can Single-Word Messages Outperform Pictograms?. Human factors. 60. 18720817751623. 10.1177/0018720817751623.
- P. Bodala, Indu & I. Abbasi, Nida & Sun, Yu & Bezerianos, Anastasios & Al-Nashash, Hasan & Thakor, N.v. (2017). Measuring vigilance decrement using computer vision assisted eye tracking in dynamic naturalistic environments. Conference proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 2017. 2478-2481. 10.1109/EMBC.2017.8037359.
- D. Tran et al., “A collaborative control framework for driver assistance systems,” 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, pp. 6038-6043. doi: 10.1109/ICRA.2017.7989715
- D. Osipychev, Duy Tran, Weihua Sheng and G. Chowdhary. 2017. “Human intention-based collision avoidance for autonomous cars,” 2017 American Control Conference (ACC), Seattle, WA, pp. 2974-2979. doi: 10.23919/ACC.2017.7963403
- Jakob Rodseth, Edward P. Washabaugh, Ali Al Haddad, Paula Kartje, Denise G. Tate, Chandramouli Krishnan, A novel low-cost solution for driving assessment in individuals with and without disabilities, Applied Ergonomics, Volume 65, 2017, Pages 335-344.
- D. Tran, E. Tadesse, W. Sheng, Y. Sun, M. Liu and S. Zhang, “A driver assistance framework based on driver drowsiness detection,” 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), Chengdu, 2016, pp. 173-178. doi: 10.1109/CYBER.2016.7574817.
- Super, S; Aminuddin, M. M. M. Practicability of Meaningful Sound to Avoid Attention-Drifting Phenomenon While Driving. Advanced Science Letters, Volume 22, Number 9, September 2016, pp. 2138-2140(3)
- T. Hwang, M. Kim, S. Hong and K. S. Park, “Driver drowsiness detection using the in-ear EEG,” 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016, pp. 4646-4649. doi: 10.1109/EMBC.2016.75917.
- More information about academic work can be found on ResearchGate…….
Examples of earlier publications
The scenario generation, traffic generation, data analyses and storage and real time simulation software is based on the research simulator software that was developed in the 90ties at the University of Groningen. A number of examples of publications based on an earlier version of this simulator software:
- Van Winsum. W., (1998). Preferred time headway in car-following and individual differences in perceptual-motor skills. Perceptual and Motor Skills, 87, 863-873.
- van Winsum. W., de Waard, D. & Brookhuis, K.A. (1999). Lane change manoeuvres and safety margins. Transportation Research Part F 2(1999), 139-149.
- van Winsum. W., Brookhuis, K.A. & de Waard, D. (1999). A comparison of different ways to approximate Time-to-Line Crossing (TLC) during car driving. Accident Analysis and Prevention, 32, 57-56.
- Van Winsum, W. & Brouwer, W. (1997). Time headway in car following and operational performance during unexpected braking.Perceptual and Motor Skills, 84, 1247-1257.
- Van Winsum, W. & Heino, A. (1996). Choice of time-headway in car-following and the role of time-to-collision information in braking. Ergonomics, 39(4), 579-592.
- Van Winsum, W. & Godthelp, H. (1996). Speed choice and steering behavior in curve driving. Human Factors, 38(3), 434-441.
Since then this software has been extended and especially the graphics has been improved substantially . It has laid the groundworks for numerous scientific publications by researchers all over the world.
can be found everywhere in the world, for example:
– University Hospital Bern (Switserland)
– University of Nottingham (UK)
– Oxford Brookes University (UK)
– Healthlink Hong Kong
– University of South Carolina (US)
– University of Oklahoma (US)
– University of Thessaly (Greece)
– Deutsche Sporthochschule Koln (Germany)
– Technische Universitat Chemnitz (Germany)
– University Hospital Seoul (Korea)
– University of Valencia (Spain)
– TMU Taipei (Taiwan)
– NTNU Trondheim (Norway)
– National University of Singapore
– Qatar University
and many more.
The script language allows you to access a wide range of variables during runtime and gives you all the flexibility you need. Installation on your computer is included in the price.
A support voucher gives access to 5 hours of paid support. A support voucher costs €350,-. The user can purchase a support voucher any time and the 5 hours of support can be freely used when the user wants. After the 5 hours of support are used, the user will be notified via email. The support voucher expires 2 years after the data of purchase. The following types of support are included:
- Carnetsoft will answer questions, via email, concerning the use of existing functionality within 2 working days (except for holiday periods). This may refer to how to create or modify graphical databases (Virtual Environments), use script functions to define an experiment, traffic, interactions between the simulator and the user or external programs, data storage and analysis etc.
- Carnetsoft will provide scripts for experiments or debug scripts provided by the user.
- Carnetsoft will modify existing databases (Virtual Environments) or make new databases according to the specifications of the user.
Carnetsoft can also develop complete behavioural experiments for you: if you want to have your experiment developed by an experienced researcher/developer, Carnetsoft can do that for you. Because of the in-house development and experience this can often be done faster and cheaper than when you create the experiments yourself. So if you are in a hurry or if you need the skills of an experienced developer. This concerns:
- creation/modification of visual databases (virtual environments)
- creation of scenario generation scripts
- creation of subject and data specification files, so have all experiment files for all subjects and conditions ready to use
- modification of runtime simulation and graphics software
If you send the specifications of the experiment you’ll receive an estimation of the required development time and a quote. As an indication of the cost involved: an experiment normally can be prepared by Carnetsoft in 20 to 40 man hours which amounts to a price of €1500 to €3000. Included are the sources of the experiment scenario scripts and databases, you you can always modify them.
Information in Dutch (rijsimulator) can be found on a separate page.
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